Department of Mathematical Sciences, University of Copenhagen, Copenhagen, Denmark.

2

Burnet Institute, Melbourne, VIC, Australia.

3

Institute for Global Health, University College London, London, UK.

4

Clinical Operational Research Unit, Department of Mathematics, University College London, London, UK.

5

Department of Applied Health Research, University College London, London, UK.

6

Department of Global Health and Development, Faculty of Public Health and Policy, London School of Hygiene & Tropical Medicine, London, UK.

7

Institute of Global Health, University of Geneva, Geneva, Switzerland.

8

Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland.

9

Institute of Mathematical Statistics and Actuarial Science, University of Bern, Bern, Switzerland.

10

Institute of Mathematics, University of Zurich, Zurich, Switzerland.

11

School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia.

12

School of Physics, University of Sydney, Sydney, NSW, Australia.

13

The Kirby Institute, UNSW Sydney, Sydney, NSW, Australia.

14

The World Bank Group, Washington, DC, USA.

Abstract

INTRODUCTION:

With limited funds available, meeting global health targets requires countries to both mobilize and prioritize their health spending. Within this context, countries have recognized the importance of allocating funds for HIV as efficiently as possible to maximize impact. Over the past six years, the governments of 23 countries in Africa, Asia, Eastern Europe and Latin America have used the Optima HIV tool to estimate the optimal allocation of HIV resources.

METHODS:

Each study commenced with a request by the national government for technical assistance in conducting an HIV allocative efficiency study using Optima HIV. Each study team validated the required data, calibrated the Optima HIV epidemic model to produce HIV epidemic projections, agreed on cost functions for interventions, and used the model to calculate the optimal allocation of available funds to best address national strategic plan targets. From a review and analysis of these 23 country studies, we extract common themes around the optimal allocation of HIV funding in different epidemiological contexts.

RESULTS AND DISCUSSION:

The optimal distribution of HIV resources depends on the amount of funding available and the characteristics of each country's epidemic, response and targets. Universally, the modelling results indicated that scaling up treatment coverage is an efficient use of resources. There is scope for efficiency gains by targeting the HIV response towards the populations and geographical regions where HIV incidence is highest. Across a range of countries, the model results indicate that a more efficient allocation of HIV resources could reduce cumulative new HIV infections by an average of 18% over the years to 2020 and 25% over the years to 2030, along with an approximately 25% reduction in deaths for both timelines. However, in most countries this would still not be sufficient to meet the targets of the national strategic plan, with modelling results indicating that budget increases of up to 185% would be required.

CONCLUSIONS:

Greater epidemiological impact would be possible through better targeting of existing resources, but additional resources would still be required to meet targets. Allocative efficiency models have proven valuable in improving the HIV planning and budgeting process.

The relationship between the share of infections in a particular population/district, and the share of the HIV budget for prevention programmes. Results pertain to the year for which latest National AIDS Spending Accounts were available at the time study was conducted – these years are presented in Table . The share of infections by sub‐population was not available for Peru, Mexico, Colombia, Argentina, Tajikistan or Ukraine. (a) PWID across 17 countries, (b) FSW across 17 countries, (c) MSM across 17 countries.

Allocations of HIV budgets prior to Optima HIV study (left bars), the mathematically optimal allocation recommended by the Optima HIV analysis (middle bars) and the allocation that was adopted by the country after the budgeting process was complete (right bars). (a) Sudan (b) Belarus. Note that in Sudan, the total budget envelope was decreased from US$12.3 m to US$9.9 m.